Source code for clipper_admin.deployers.python

from __future__ import print_function, with_statement, absolute_import

import logging
import shutil
from ..version import __version__

from .deployer_utils import save_python_function

logger = logging.getLogger(__name__)


[docs]def create_endpoint( clipper_conn, name, input_type, func, default_output="None", version=1, slo_micros=3000000, labels=None, registry=None, base_image="clipper/python-closure-container:{}".format(__version__), num_replicas=1): """Registers an application and deploys the provided predict function as a model. Parameters ---------- clipper_conn : :py:meth:`clipper_admin.ClipperConnection` A ``ClipperConnection`` object connected to a running Clipper cluster. name : str The name to be assigned to both the registered application and deployed model. input_type : str The input_type to be associated with the registered app and deployed model. One of "integers", "floats", "doubles", "bytes", or "strings". func : function The prediction function. Any state associated with the function will be captured via closure capture and pickled with Cloudpickle. default_output : str, optional The default output for the application. The default output will be returned whenever an application is unable to receive a response from a model within the specified query latency SLO (service level objective). The reason the default output was returned is always provided as part of the prediction response object. Defaults to "None". version : str, optional The version to assign this model. Versions must be unique on a per-model basis, but may be re-used across different models. slo_micros : int, optional The query latency objective for the application in microseconds. This is the processing latency between Clipper receiving a request and sending a response. It does not account for network latencies before a request is received or after a response is sent. If Clipper cannot process a query within the latency objective, the default output is returned. Therefore, it is recommended that the SLO not be set aggressively low unless absolutely necessary. 100000 (100ms) is a good starting value, but the optimal latency objective will vary depending on the application. labels : list(str), optional A list of strings annotating the model. These are ignored by Clipper and used purely for user annotations. registry : str, optional The Docker container registry to push the freshly built model to. Note that if you are running Clipper on Kubernetes, this registry must be accessible to the Kubernetes cluster in order to fetch the container from the registry. base_image : str, optional The base Docker image to build the new model image from. This image should contain all code necessary to run a Clipper model container RPC client. num_replicas : int, optional The number of replicas of the model to create. The number of replicas for a model can be changed at any time with :py:meth:`clipper.ClipperConnection.set_num_replicas`. """ clipper_conn.register_application(name, input_type, default_output, slo_micros) deploy_python_closure(clipper_conn, name, version, input_type, func, base_image, labels, registry, num_replicas) clipper_conn.link_model_to_app(name, name)
[docs]def deploy_python_closure( clipper_conn, name, version, input_type, func, base_image="clipper/python-closure-container:{}".format(__version__), labels=None, registry=None, num_replicas=1): """Deploy an arbitrary Python function to Clipper. The function should take a list of inputs of the type specified by `input_type` and return a Python list or numpy array of predictions as strings. Parameters ---------- clipper_conn : :py:meth:`clipper_admin.ClipperConnection` A ``ClipperConnection`` object connected to a running Clipper cluster. name : str The name to be assigned to both the registered application and deployed model. version : str The version to assign this model. Versions must be unique on a per-model basis, but may be re-used across different models. input_type : str The input_type to be associated with the registered app and deployed model. One of "integers", "floats", "doubles", "bytes", or "strings". func : function The prediction function. Any state associated with the function will be captured via closure capture and pickled with Cloudpickle. base_image : str, optional The base Docker image to build the new model image from. This image should contain all code necessary to run a Clipper model container RPC client. labels : list(str), optional A list of strings annotating the model. These are ignored by Clipper and used purely for user annotations. registry : str, optional The Docker container registry to push the freshly built model to. Note that if you are running Clipper on Kubernetes, this registry must be accesible to the Kubernetes cluster in order to fetch the container from the registry. num_replicas : int, optional The number of replicas of the model to create. The number of replicas for a model can be changed at any time with :py:meth:`clipper.ClipperConnection.set_num_replicas`. Example ------- Define a pre-processing function ``center()`` and train a model on the pre-processed input:: from clipper_admin import ClipperConnection, DockerContainerManager from clipper_admin.deployers.python import deploy_python_closure import numpy as np import sklearn clipper_conn = ClipperConnection(DockerContainerManager()) # Connect to an already-running Clipper cluster clipper_conn.connect() def center(xs): means = np.mean(xs, axis=0) return xs - means centered_xs = center(xs) model = sklearn.linear_model.LogisticRegression() model.fit(centered_xs, ys) # Note that this function accesses the trained model via closure capture, # rather than having the model passed in as an explicit argument. def centered_predict(inputs): centered_inputs = center(inputs) # model.predict returns a list of predictions preds = model.predict(centered_inputs) return [str(p) for p in preds] deploy_python_closure( clipper_conn, name="example", input_type="doubles", func=centered_predict) """ serialization_dir = save_python_function(name, func) logger.info("Python closure saved") # Deploy function clipper_conn.build_and_deploy_model(name, version, input_type, serialization_dir, base_image, labels, registry, num_replicas) # Remove temp files shutil.rmtree(serialization_dir)